Advancing Predictive Healthcare: A Systematic Review of Transformer Models in Electronic Health Records DOI Creative Commons
Azza Abdel Monem,

Reem AlAleeli,

Khaled Shaalan

et al.

Computers, Journal Year: 2025, Volume and Issue: 14(4), P. 148 - 148

Published: April 14, 2025

This systematic study seeks to evaluate the use and impact of transformer models in healthcare domain, with a particular emphasis on their usefulness tackling key medical difficulties performing critical natural language processing (NLP) functions. The research questions focus how these can improve clinical decision-making through information extraction predictive analytics. Our findings show that models, especially applications like named entity recognition (NER) data analysis, greatly increase accuracy efficiency unstructured data. Notably, case studies demonstrated 30% boost notes 90% detection rate for malignancies imaging. These contributions emphasize revolutionary potential healthcare, therefore importance enhancing resource management patient outcomes. Furthermore, this paper emphasizes significant obstacles, such as reliance restricted datasets need format standardization, provides road map future applicability performance real-world settings.

Language: Английский

Advancing Predictive Healthcare: A Systematic Review of Transformer Models in Electronic Health Records DOI Creative Commons
Azza Abdel Monem,

Reem AlAleeli,

Khaled Shaalan

et al.

Computers, Journal Year: 2025, Volume and Issue: 14(4), P. 148 - 148

Published: April 14, 2025

This systematic study seeks to evaluate the use and impact of transformer models in healthcare domain, with a particular emphasis on their usefulness tackling key medical difficulties performing critical natural language processing (NLP) functions. The research questions focus how these can improve clinical decision-making through information extraction predictive analytics. Our findings show that models, especially applications like named entity recognition (NER) data analysis, greatly increase accuracy efficiency unstructured data. Notably, case studies demonstrated 30% boost notes 90% detection rate for malignancies imaging. These contributions emphasize revolutionary potential healthcare, therefore importance enhancing resource management patient outcomes. Furthermore, this paper emphasizes significant obstacles, such as reliance restricted datasets need format standardization, provides road map future applicability performance real-world settings.

Language: Английский

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